#Microsoft edge update slow computer how to
Next, learn how to scale up from existing PersonGroup and FaceList objects to LargePersonGroup and LargeFaceList objects, respectively.Many users have reported that their PC is running extremely slow after installing the Windupdate, or after installing the new Chromium-based version of the Microsoft Edge browser. In this guide, you learned how to mitigate latency when using the Face service.
If longer latencies affect the user experience, choose a timeout threshold (for example, maximum 5 seconds) before retrying the API call.If you need to call multiple service methods, consider calling them in parallel if your application design allows for it.When you create your Face subscription, make sure to choose the region closest to where your application is hosted.If your computer has a slow connection to the Face service, this will affect the response time of service methods. Slow connection between your compute resource and the Face service IEnumerable results = faces_1.Result.Concat (faces_2.Result) For example, if you need to detect faces in two images to perform a face comparison: Faces are ranked by face rectangle size from large to small. The Face service can return up to 100 faces for an image. The performance of the DetectWithUrlAsync and DetectWithStreamAsync methods also depends on how many faces are in an image.For face recognition, reducing the face size to 200x200 pixels doesn't affect the accuracy of the recognition model.When you use detection model DetectionModel.Detection02, reducing the image file size will only increase processing speed if the image file is smaller than 1920x1080. For face detection, when using detection model DetectionModel.Detection01, reducing the image file size will increase processing speed.See the guidelines regarding input data for face detection and input data for face recognition.Consider storing the image in Azure Premium Blob Storage.It takes the service longer to process the file, in proportion to the file size.If the file to upload is large, that will impact the response time of the DetectWithStreamAsync method, for the following reasons: using FileStream fs = faces = await (fs, detectionModel: DetectionModel.Detection02)
For example, when you call the DetectWithStreamAsync method of the Face service, you can upload an image in which the service tries to detect faces. Some Azure services provide methods that obtain data from a file that you upload. To mitigate this situation, consider storing the image in Azure Premium Blob Storage. If the connection from the Face service to the remote server is slow, that will affect the response time of the Detect method.
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The Face service must then download the image from the remote server. For example, when you call the DetectWithUrlAsync method of the Face service, you can specify the URL of an image in which the service tries to detect faces. Some Azure services provide methods that obtain data from a remote URL that you provide. Possible causes of latency Slow connection between the Cognitive Service and a remote URL Azure Cognitive Services does not provide any Service Level Agreement (SLA) regarding latency.
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